scholarly journals A multi-method autonomous assessment of primary productivity and export efficiency in the springtime North Atlantic

2018 ◽  
Author(s):  
Nathan Briggs ◽  
Kristinn Guðmundsson ◽  
Ivona Cetinić ◽  
Eric D'Asaro ◽  
Eric Rehm ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Carbon Cycle ◽  
North Atlantic ◽  
Primary Productivity ◽  
Gross Primary Productivity ◽  
Test Method ◽  
Significant Advance ◽  
Diatom Bloom ◽  
Diel Cycles ◽  
Parameterized Model

Abstract. Fixation of organic carbon by phytoplankton is the foundation of nearly all open-ocean ecosystems and a critical part of the global carbon cycle. But quantification and validation of ocean primary productivity at large scale remains a major challenge, due to limited coverage of ship-based measurements and the difficulty of validating diverse measurement techniques. Accurate primary productivity measurements from autonomous platforms would be highly desirable, due to much greater potential coverage. In pursuit of this goal we estimate gross primary productivity over two months in the springtime North Atlantic from an autonomous Lagrangian float using diel cycles of particulate organic carbon derived from optical beam attenuation. We test method precision and accuracy by comparison against entirely independent estimates from a locally parameterized model based on chlorophyll α and light measurements from the same float. During nutrient replete conditions (80 % of the study period), we obtain strong relative agreement between the independent methods across an order of magnitude of productivities (r2 = 0.97), with slight under-estimation by the diel cycles method (−19 ± 5 %). At the end of the diatom bloom, this relative difference increases to −58 % for a six-day period, likely a response to SiO4 limitation, which is not included in the model. In addition, we estimate gross oxygen productivity from O2 diel cycles and find strong correlation with diel cycles-based gross primary productivity over the entire deployment, providing further qualitative support to both methods. Finally, simultaneous estimates of net community productivity, carbon export and particle size suggest that bloom growth is halted by a combination of reduced productivity due to SiO4 limitation and increased export efficiency due to rapid aggregation. After the diatom bloom, high chlorophyll α normalized productivity indicates that low net growth during this period is due to increased heterotrophic respiration and not nutrient limitation. These findings represent a significant advance in the accuracy and completeness of upper ocean carbon cycle measurements from an autonomous platform.

scholarly journals A multi-method autonomous assessment of primary productivity and export efficiency in the springtime North Atlantic

2018 ◽  
Vol 15 (14) ◽  
pp. 4515-4532 ◽  
Author(s):  
Nathan Briggs ◽  
Kristinn Guðmundsson ◽  
Ivona Cetinić ◽  
Eric D'Asaro ◽  
Eric Rehm ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Carbon Cycle ◽  
North Atlantic ◽  
Primary Productivity ◽  
Gross Primary Productivity ◽  
Test Method ◽  
Diel Cycle ◽  
Significant Advance ◽  
Diatom Bloom ◽  
Diel Cycles

Abstract. Fixation of organic carbon by phytoplankton is the foundation of nearly all open-ocean ecosystems and a critical part of the global carbon cycle. But the quantification and validation of ocean primary productivity at large scale remains a major challenge due to limited coverage of ship-based measurements and the difficulty of validating diverse measurement techniques. Accurate primary productivity measurements from autonomous platforms would be highly desirable due to much greater potential coverage. In pursuit of this goal we estimate gross primary productivity over 2 months in the springtime North Atlantic from an autonomous Lagrangian float using diel cycles of particulate organic carbon derived from optical beam attenuation. We test method precision and accuracy by comparison against entirely independent estimates from a locally parameterized model based on chlorophyll a and light measurements from the same float. During nutrient-replete conditions (80 % of the study period), we obtain strong relative agreement between the independent methods across an order of magnitude of productivities (r2=0.97), with slight underestimation by the diel cycle method (−19 ± 5 %). At the end of the diatom bloom, this relative difference increases to −58 % for a 6-day period, likely a response to SiO4 limitation, which is not included in the model. In addition, we estimate gross oxygen productivity from O2 diel cycles and find strong correlation with diel-cycle-based gross primary productivity over the entire deployment, providing further qualitative support for both methods. Finally, simultaneous estimates of net community productivity, carbon export, and particle size suggest that bloom growth is halted by a combination of reduced productivity due to SiO4 limitation and increased export efficiency due to rapid aggregation. After the diatom bloom, high Chl a-normalized productivity indicates that low net growth during this period is due to increased heterotrophic respiration and not nutrient limitation. These findings represent a significant advance in the accuracy and completeness of upper-ocean carbon cycle measurements from an autonomous platform.

scholarly journals New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity

2011 ◽  
Vol 38 (17) ◽  
pp. n/a-n/a ◽  
Author(s):  
Christian Frankenberg ◽  
Joshua B. Fisher ◽  
John Worden ◽  
Grayson Badgley ◽  
Sassan S. Saatchi ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Primary Productivity ◽  
Gross Primary Productivity ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle

scholarly journals Investigating the usefulness of satellite derived fluorescence data in inferring gross primary productivity within the carbon cycle data assimilation system

2015 ◽  
Vol 12 (1) ◽  
pp. 707-749 ◽  
Author(s):  
E. N. Koffi ◽  
P. J. Rayner ◽  
A. J. Norton ◽  
C. Frankenberg ◽  
M. Scholze
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Primary Productivity ◽  
Chlorophyll Concentration ◽  
Gross Primary Productivity ◽  
Combined Model ◽  
Leaf Chlorophyll ◽  
Absorbed Photosynthetically Active Radiation ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We investigate the utility of satellite measurements of chlorophyll fluorescence (Fs) in constraining gross primary productivity (GPP). We ingest Fs measurements into the Carbon-Cycle Data Assimilation System (CCDAS) which has been augmented by the fluorescence component of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. CCDAS simulates well the patterns of Fs suggesting the combined model is capable of ingesting these measurements. However simulated Fs is insensitive to the key parameter controlling GPP, the carboxylation capacity (Vcmax). Simulated Fs is sensitive to both the incoming absorbed photosynthetically active radiation (aPAR) and leaf chlorophyll concentration both of which are treated as perfectly known in previous CCDAS versions. Proper use of Fs measurements therefore requires enhancement of CCDAS to include and expose these variables.

scholarly journals Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set

2014 ◽  
Vol 11 (8) ◽  
pp. 2185-2200 ◽  
Author(s):  
M. Verma ◽  
M. A. Friedl ◽  
A. D. Richardson ◽  
G. Kiely ◽  
A. Cescatti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Cycle ◽  
Primary Productivity ◽  
Remotely Sensed ◽  
Gross Primary Productivity ◽  
Spatiotemporal Variability ◽  
Variable Component ◽  
Carbon Budgets ◽  
Data Set ◽  
Flux Tower

Abstract. Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET La Thuile data set, which includes several times more sites (144) and site years (422) than previous studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10–80%). The ability of remotely sensed proxies and models to explain interannual variability in GPP was even more limited. Remotely sensed proxies explained 40–60% of interannual variance in annual GPP in moisture-limited biomes, including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux tower GPP, and difference between the footprints of MODIS pixels and flux tower measurements are acknowledged as unresolved challenges.

scholarly journals Investigating the usefulness of satellite-derived fluorescence data in inferring gross primary productivity within the carbon cycle data assimilation system

2015 ◽  
Vol 12 (13) ◽  
pp. 4067-4084 ◽  
Author(s):  
E. N. Koffi ◽  
P. J. Rayner ◽  
A. J. Norton ◽  
C. Frankenberg ◽  
M. Scholze
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Primary Productivity ◽  
Carbon Fluxes ◽  
Gross Primary Productivity ◽  
Sensitivity Tests ◽  
Parameter Values ◽  
Data Assimilation System ◽  
Scope Model ◽  
Assimilation System

Abstract. Simulations of carbon fluxes with terrestrial biosphere models still exhibit significant uncertainties, in part due to the uncertainty in model parameter values. With the advent of satellite measurements of solar induced chlorophyll fluorescence (SIF), there exists a novel pathway for constraining simulated carbon fluxes and parameter values. We investigate the utility of SIF in constraining gross primary productivity (GPP). As a first test we assess whether SIF simulations are sensitive to important parameters in a biosphere model. SIF measurements at the wavelength of 755 nm are simulated by the Carbon-Cycle Data Assimilation System (CCDAS) which has been augmented by the fluorescence component of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. Idealized sensitivity tests of the SCOPE model stand-alone indicate strong sensitivity of GPP to the carboxylation capacity (Vcmax) and of SIF to the chlorophyll AB content (Cab) and incoming short wave radiation. Low sensitivity is found for SIF to Vcmax, however the relationship is subtle, with increased sensitivity under high radiation conditions and lower Vcmax ranges. CCDAS simulates well the patterns of satellite-measured SIF suggesting the combined model is capable of ingesting the data. CCDAS supports the idealized sensitivity tests of SCOPE, with SIF exhibiting sensitivity to Cab and incoming radiation, both of which are treated as perfectly known in previous CCDAS versions. These results demonstrate the need for careful consideration of Cab and incoming radiation when interpreting SIF and the limitations of utilizing SIF to constrain Vcmax in the present set-up in the CCDAS system.

scholarly journals Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile dataset

2013 ◽  
Vol 10 (7) ◽  
pp. 11627-11669 ◽  
Author(s):  
M. Verma ◽  
M. A. Friedl ◽  
A. D. Richardson ◽  
G. Kiely ◽  
A. Cescatti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Cycle ◽  
Interannual Variability ◽  
Primary Productivity ◽  
Remotely Sensed ◽  
Gross Primary Productivity ◽  
Spatiotemporal Variability ◽  
Variable Component ◽  
Carbon Budgets ◽  
Data Set

Abstract. Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET "La Thuile" data set, which includes several times more sites (144) and site years (422) than previous efforts have used. Our results show that remotely sensed proxies and modeled GPP are able to capture statistically significant amounts of spatial variation in mean annual GPP in every biome except croplands, but that the total variance explained differed substantially across biomes (R2 ≈ 0.1−0.8). The ability of remotely sensed proxies and models to explain interannual variability GPP was even more limited. Remotely sensed proxies explained 40–60% of interannual variance in annual GPP in moisture-limited biomes including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, and deciduous broadleaf forests. Because important factors that affect year-to-year variation in GPP are not explicitly captured or included in the remote sensing proxies and models we examined (e.g., interactions between biotic and abiotic conditions, and lagged ecosystems responses to environmental process), our results are not surprising. Nevertheless, robust and repeatable characterization of interannual variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. As larger and more comprehensive data sets derived from the FLUXNET community become available, additional systematic assessment and refinement of remote sensing-based methods for monitoring annual GPP is warranted.

scholarly journals Terrestrial Carbon Cycle Variability

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2371 ◽  
Author(s):  
Dennis Baldocchi ◽  
Youngryel Ryu ◽  
Trevor Keenan
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Carbon Cycle ◽  
Primary Productivity ◽  
Carbon Sink ◽  
Global Scale ◽  
Gross Primary Productivity ◽  
Arid Ecosystems ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle

A growing literature is reporting on how the terrestrial carbon cycle is experiencing year-to-year variability because of climate anomalies and trends caused by global change. As CO2 concentration records in the atmosphere exceed 50 years and as satellite records reach over 30 years in length, we are becoming better able to address carbon cycle variability and trends. Here we review how variable the carbon cycle is, how large the trends in its gross and net fluxes are, and how well the signal can be separated from noise. We explore mechanisms that explain year-to-year variability and trends by deconstructing the global carbon budget. The CO2 concentration record is detecting a significant increase in the seasonal amplitude between 1958 and now. Inferential methods provide a variety of explanations for this result, but a conclusive attribution remains elusive. Scientists have reported that this trend is a consequence of the greening of the biosphere, stronger northern latitude photosynthesis, more photosynthesis by semi-arid ecosystems, agriculture and the green revolution, tropical temperature anomalies, or increased winter respiration. At the global scale, variability in the terrestrial carbon cycle can be due to changes in constituent fluxes, gross primary productivity, plant respiration and heterotrophic (microbial) respiration, and losses due to fire, land use change, soil erosion, or harvesting. It remains controversial whether or not there is a significant trend in global primary productivity (due to rising CO2, temperature, nitrogen deposition, changing land use, and preponderance of wet and dry regions). The degree to which year-to-year variability in temperature and precipitation anomalies affect global primary productivity also remains uncertain. For perspective, interannual variability in global gross primary productivity is relatively small (on the order of 2 Pg-C y-1) with respect to a large and uncertain background (123 +/- 4 Pg-C y-1), and detected trends in global primary productivity are even smaller (33 Tg-C y-2). Yet residual carbon balance methods infer that the terrestrial biosphere is experiencing a significant and growing carbon sink. Possible explanations for this large and growing net land sink include roles of land use change and greening of the land, regional enhancement of photosynthesis, and down regulation of plant and soil respiration with warming temperatures. Longer time series of variables needed to provide top-down and bottom-up assessments of the carbon cycle are needed to resolve these pressing and unresolved issues regarding how, why, and at what rates gross and net carbon fluxes are changing.

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